The present invention generally relates to the field of signal processing and, more particularly, to sonar processors and pulse Doppler radars utilizing frequency domain processing to improve signal detection.
Since the invention of radar and sonar for detection of objects, numerous techniques for signal processing have been introduced to enhance the image obtained from the returned signal and to reduce the noise inherent in signal reflection within a fluid media. Recent coherent systems have improved the imaging of the object to obtain higher quality, or higher resolution, signatures of the object for purposes of identification. Further, developments in linear detectors have enhanced receiver performance, but low signal to noise ratios pose detection difficulties for linear detectors. However, clutter and noise remain major obstacles to improving the performance of these systems.
When signal frequency is known to exist within a particular range of frequencies, the processing receiver must employ a set of parallel filters to cover the known frequency ranges. The typical fast Fourier transform (FFT) processing receiver is illustrated in FIG. 1. As shown in FIG. 1, the processing receiver 10 receives an input signal equal to signal plus noise. The analog signal is initially filtered by filter 11 and run through an A/D converter 12. The signal is buffered into N samples in buffer 13, and for a single range bin sample this is the number of samples used in the weighting and FFT. Each buffered signal is then weighted in weighting element 14, and the FFT processor 15 performs the fast Fourier transform and provides the signal into a real portion, Rx, and an imaginary portion, Ix.
The real and imaginary portions are squared and summed, and the square root of this sum is taken within the linear detector 16. The resultant signal from the linear detector 16 represents the signal amplitude in filter x, which is integrated in integrator 17. The average noise level computation block 18 computes the average noise level based on the integrated amplitude. The integrated amplitude and the average noise level are evaluated to determine if the signal exceeds a false alarm threshold in false alarm detector 19, and this signal is transmitted through the system to determine target classification, location, tracking, frequency accuracy improvement, and field signal correlation.
The problems with the aforementioned design are that performance of the receiver integrator 17 can generally be limited by the inherent properties associated with detector square law performance for low signal-to-noise ratio situations. Additionally, if the non-coherent integration time exceeds the time duration that the signal remains within a single filter bandwidth, performance is lost. Performance loss may also occur if integrators are loaded with noise before a signal occurs.
System performance is highly dependent on many factors associated with the system illustrated in FIG. 1. For example, the position of the signal within the filter affects performance of the system, with maximum performance loss occurring at 0.5 (0.5 distance to next filter center) offset from filter center. Further, the weighting function used to maximize off filter center performance can have significant implications on signal detection. Frequency accuracy is generally determined by filter resolution, and for filter offsets in the 0.5 area, accuracy uncertainty is two filters. The most advanced signal processors have been shown to improve frequency accuracy by at most a factor of five.
The function of signal tracking in general may require performance of one to two dB above MDL (Minimal Detection Levels) for effective performance. Other influences may also affect system performance, including contamination from tilted noise spectra, noise spiking, spurious other signals, and other environmental unknowns which can bias the system threshold and cause performance loss. Internal balancing of system parameters is also critical, in that the accuracy required to determine the threshold level increases with increasing time constants, while at the same time increasing these time constants increases the losses associated with several parameters, such as environment, filter offset, and signal position within the filter.
Pulse Doppler radars, such as those used on fighter aircraft, have traditionally relied on FFT processing without the use of post detection integration to improve signal detection. Any FFT detection improvement obtained is proportional to the resolution used and is driven by the pulse repetition frequency (PRF) employed by the radar as well as the dwell time spent on the target, or the time when the radar is illuminating the target. Pulse Doppler radars utilize either high PRF or medium PRF to detect targets. The advantage of high PRF is that the higher PRF frequency provides greater detection ranges than medium PRF, with high PRF providing very poor range resolution, range accuracy and ghosting, wherein multiple targets appear within the same beam width.
Medium PRF radars provide good range and range rate resolution but provide inferior detection ranges. The key factors affecting medium PRF radar detection performance are that time must budgeted during dwell time on the target to obtain independent measurements on the target, thereby permitting resolution of range and Doppler ambiguity, as well as sidelobe clutter (slc), which is driven by antennae side lobes, range resolution, and other extraneous factors. The time allocated to resolve these issues takes away the time that could be used to integrate the signal longer for better detection.
Range/Doppler effects are mitigated by different PRF approaches, and the number of PRFs used varies with different radars. The most popular method uses eight PRFs during the dwell. When scanning over a target, the radar must receive a detection on three of the eight PRFs in order to resolve ambiguities. Variance in the PRFs by a factor of two without correcting for average radiated power reduces medium PRF average power on the target, again diminishing the performance of medium PRF with respect to high PRF.
A modulated transmitted pulse is used to obtain range information from high PRF. During radar processing of echoes, the modulation is utilized to determine target range. This approach requires three periods in the dwell to resolve detection, thereby providing 2.6 more periods of time to collect coherent data for processing. This additional collection time contributes to higher high PRF performance. In addition, since the high PRF is generally an order of magnitude higher than medium PRF, it essentially eliminates Doppler ambiguity and provides the high PRF mode with a factor of three improvement in FFT resolution, again contributing to enhanced performance.
The poor range resolution/accuracy and ghosting associated with multiple targets in the beam requires multiple scans before the accuracy of the data is good enough be used for targeting weapons. A Kalman type filter can be used to get the improvements in resolution and accuracy needed. A minimum of three looks is generally used for the data to be considered adequate to pass on to the mission computer. Medium PRF, on the other hand, has much higher quality range information on one scan but does nothing to improve azimuth accuracy. Medium PRF would therefore also require additional looks to have sufficient data quality to support fighter engagement. However, since the long range detection performance of medium PRF is considerably less than that of high PRF, medium PRF is not generally used in long range air-to-air detection operations.
The significant problems or constraints associated with fighter or other airborne radar operations are as follows. Medium PRF average power decreases as the PRF is lowered if pulse width is not adjusted. Pulse compression signal losses occur as a function of Doppler velocity, and thus the higher the velocity the larger the losses. The specific losses are related to the pulse widths employed. The 3 of 8 design logic used to resolve range and Doppler ambiguity in medium PRF operation wastes time that could be used to improve signal detection. High PRF uses 3 of 3 detection, reducing the time loss by a factor of 2.6 but loses some of this time due to the waveform used to determine range. Short dwell times and PRFs used limit the number of Doppler filters provided. Typically, medium PRF provides thirty-two Doppler filters. Frequency accuracy is limited by the resolution of the Doppler filter. The performance of the system is dependent on the position of the signal within the Doppler filter, and losses occur when the signals are off-of-filter center. Determining the noise level power in the vicinity of the signal is subject to real world contamination from tilted noise spectra, noise spiking, other signals and a host of unknowns which can bias the Constant False Alarm Rate (CFAR) threshold and cause performance losses. The quality of data provided to the General Purpose Computer (GPC) for tracking requires a number of scans for the GPC to determine accurate target position, heading and ground velocity. The radar limitations prohibit it from determining the target heading and ground velocity.
Examples of prior art radar systems are shown in the following U.S. patents: U.S. Pat. No. 3,935,572 (Broniwitz et al.); U.S. Pat. No. 4,093,948 (Long, III); U.S. Pat. No. 4,106,019 (Alexander et al.); U.S. Pat. No. 4,499,467 (Rittenbach); U.S. Pat. No. 4,584,579 (Frost et al.); U.S. Pat. No. 4,746,922 (Prenat); and U.S. Pat. No. 4,954,830 (Kirkorian et al.). The entire contents of each of these patents and all other patents and other publications mentioned anywhere in this disclosure are hereby incorporated by reference.